EPIMHC: a curated database of MHC-binding peptides for customized computational vaccinology

APEX-pHLA: A novel method for accurate prediction of the binding between exogenous short peptides and HLA class I molecules

Human leukocyte antigen (HLA) molecules play critically significant role within the realm of immunotherapy due to their capacities to recognize and bind exogenous antigens such as peptides, subsequently delivering them to immune cells. Predicting the binding between peptides and HLA molecules (pHLA) can expedite the screening of immunogenic peptides and facilitate vaccine design. However, traditional experimental methods are time-consuming and inefficient. In this study, an efficient method based on deep learning was developed for predicting peptide-HLA binding, which treated peptide sequences as linguistic entities. It combined the architectures of textCNN and BiLSTM to create a deep neural network model called APEX-pHLA. This model operated without limitations related to HLA class I allele variants and peptide segment lengths, enabling efficient encoding of sequence features for both HLA and peptide segments. On the independent test set, the model achieved Accuracy, ROC_AUC, F1, and MCC is 0.9449, 0.9850, 0.9453, and 0.8899, respectively. Similarly, on an external test set, the results were 0.9803, 0.9574, 0.8835, and 0.7863, respectively. These findings outperformed fifteen methods previously reported in the literature. The accurate prediction capability of the APEX-pHLA model in peptide-HLA binding might provide valuable insights for future HLA vaccine design.

An integrated database of experimentally validated major histocompatibility complex epitopes for antigen-specific cancer therapy

Cancer immunotherapy represents a paradigm shift in oncology, offering a superior anti-tumor efficacy and the potential for durable remission. The success of personalized vaccines and cell therapies hinges on the identification of immunogenic epitopes capable of eliciting an effective immune response. Current limitations in the availability of immunogenic epitopes restrict the broader application of such therapies. A critical criterion for serving as potential cancer antigens is their ability to stably bind to the major histocompatibility complex (MHC) for presentation on the surface of tumor cells. To address this, we have developed a comprehensive database of MHC epitopes, experimentally validated for their MHC binding and cell surface presentation. Our database catalogs 451 065 MHC peptide epitopes, each with experimental evidence for MHC binding, along with detailed information on human leukocyte antigen allele specificity, source peptides, and references to original studies. We also provide the grand average of hydropathy scores and predicted immunogenicity for the epitopes. The database (MHCepitopes) has been made available on the web and can be accessed at https://github.com/jcm1201/MHCepitopes.git. By consolidating empirical data from various sources coupled with calculated immunogenicity and hydropathy values, our database offers a robust resource for selecting actionable tumor antigens and advancing the design of antigen-specific cancer immunotherapies. It streamlines the process of identifying promising immunotherapeutic targets, potentially expediting the development of effective antigen-based cancer immunotherapies.

Integrating Reinforcement Learning and Monte Carlo Tree Search for enhanced neoantigen vaccine design

Recent advances in cancer immunotherapy have highlighted the potential of neoantigen-based vaccines. However, the design of such vaccines is hindered by the possibility of weak binding affinity between the peptides and the patient's specific human leukocyte antigen (HLA) alleles, which may not elicit a robust adaptive immune response. Triggering cross-immunity by utilizing peptide mutations that have enhanced binding affinity to target HLA molecules, while preserving their homology with the original one, can be a promising avenue for neoantigen vaccine design. In this study, we introduced UltraMutate, a novel algorithm that combines Reinforcement Learning and Monte Carlo Tree Search, which identifies peptide mutations that not only exhibit enhanced binding affinities to target HLA molecules but also retains a high degree of homology with the original neoantigen. UltraMutate outperformed existing state-of-the-art methods in identifying affinity-enhancing mutations in an independent test set consisting of 3660 peptide-HLA pairs. UltraMutate further showed its applicability in the design of peptide vaccines for Human Papillomavirus and Human Cytomegalovirus, demonstrating its potential as a promising tool in the advancement of personalized immunotherapy.

In Silico Tools for Predicting Novel Epitopes

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Bioinformatics and Computer Simulation approaches to the discovery and analysis of Bioactive Peptides

The traditional process of separating and purifying bioactive peptides is laborious and time-consuming. Using a traditional process to identify is difficult, and there is a lack of fast and accurate activity evaluation methods. How to extract bioactive peptides quickly and efficiently is still the focus of bioactive peptides research. In order to improve the present situation of the research, bioinformatics techniques and peptidome methods are widely used in this field. At the same time, bioactive peptides have their own specific pharmacokinetic characteristics, so computer simulation methods have incomparable advantages in studying the pharmacokinetics and pharmacokinetic-pharmacodynamic correlation models of bioactive peptides. The purpose of this review is to summarize the combined applications of bioinformatics and computer simulation methods in the study of bioactive peptides, with focuses on the role of bioinformatics in simulating the selection of enzymatic hydrolysis and precursor proteins, activity prediction, molecular docking, physicochemical properties, and molecular dynamics. Our review shows that new bioactive peptide molecular sequences with high activity can be obtained by computer-aided design. The significance of the pharmacokinetic-pharmacodynamic correlation model in the study of bioactive peptides is emphasized. Finally, some problems and future development potential of bioactive peptides binding new technologies are prospected.

The pockets guide to HLA class I molecules

Human leukocyte antigens (HLA) are cell-surface proteins that present peptides to T cells. These peptides are bound within the peptide binding cleft of HLA, and together as a complex, are recognised by T cells using their specialised T cell receptors. Within the cleft, the peptide residue side chains bind into distinct pockets. These pockets ultimately determine the specificity of peptide binding. As HLAs are the most polymorphic molecules in humans, amino acid variants in each binding pocket influences the peptide repertoire that can be presented on the cell surface. Here, we review each of the 6 HLA binding pockets of HLA class I (HLA-I) molecules. The binding specificity of pockets B and F are strong determinants of peptide binding and have been used to classify HLA into supertypes, a useful tool to predict peptide binding to a given HLA. Over the years, peptide binding prediction has also become more reliable by using binding affinity and mass spectrometry data. Crystal structures of peptide-bound HLA molecules provide a means to interrogate the interactions between binding pockets and peptide residue side chains. We find that most of the bound peptides from these structures conform to binding motifs determined from prediction software and examine outliers to learn how these HLAs are stabilised from a structural perspective.

virusMED: an atlas of hotspots of viral proteins

Metal binding sites, antigen epitopes and drug binding sites are the hotspots in viral proteins that control how viruses interact with their hosts. virusMED (virus Metal binding sites, Epitopes and Drug binding sites) is a rich internet application based on a database of atomic interactions around hotspots in 7041 experimentally determined viral protein structures. 25306 hotspots from 805 virus strains from 75 virus families were characterized, including influenza, HIV-1 and SARS-CoV-2 viruses. Just as Google Maps organizes and annotates points of interest, virusMED presents the positions of individual hotspots on each viral protein and creates an atlas upon which newly characterized functional sites can be placed as they are being discovered. virusMED contains an extensive set of annotation tags about the virus species and strains, viral hosts, viral proteins, metal ions, specific antibodies and FDA-approved drugs, which permits rapid screening of hotspots on viral proteins tailored to a particular research problem. The virusMED portal (https://virusmed.biocloud.top) can serve as a window to a valuable resource for many areas of virus research and play a critical role in the rational design of new preventative and therapeutic agents targeting viral infections.

Immunoregulatory T cell epitope peptides for the treatment of allergic disease

Allergic diseases are type 2 inflammatory reactions with an increasing worldwide prevalence, making the search for new therapeutic options pertinent. Allergen immunotherapy is the only disease-modifying approach for allergic rhinitis, though it can result in systemic reactions. Recently, peptide immunotherapy (PIT), involving T-cell epitope peptides that bind to major histocompatibility complexes, have been developed. It is speculated that they can induce T helper cell type 2 anergy, Treg cell upregulation or immune deviation. Promising results in cat dander, honeybee venom, Japanese cedar pollen, grass pollens, ragweed and house dust mite clinical trials have shown safety, efficacy and tolerability to PIT. Hence, PIT may hold the potential to change the treatment algorithm for allergic rhinitis.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

In Silico designing and immunogenic production of the multimeric CfaB*ST, CfaE, LTB antigen as a peptide vaccine against Enterotoxigenic Escherichia coli

Enterotoxigenic Escherichia coli (ETEC) is the most frequent bacterial cause of diarrhea particularly reported in children of developing countries and also travelers. Enterotoxins and colonization factor antigens (CFAs) are two major virulence factors in ETEC pathogenesis. Colonization factor antigen I (CFA/I) includes major pilin subunit CfaB, and a minor adhesive subunit (CfaE), and enterotoxins consisting of heat-labile toxin subunit B (LTB) and heat-stable toxin (ST). Chimeric proteins (CCL) carrying epitopes and adjuvant sequences increase the possibility of eliciting a broad cellular or effective immune response. In the present study, a chimeric candidate vaccine containing CfaB*ST, CfaE, and LTB (CCL) was designed via in silico techniques. This chimeric gene was synthesized by using codon usage of E. coli for increasing the expression of the recombinant protein. After designing the chimeric construct, it showed a high antigenicity index estimated by the vaxiJen server. Linear and conformational B-cell epitopes were identified and indicated suitable immunogenicity of this multimeric recombinant protein. Thermodynamic analyses for mRNA structures revealed the appropriate folding of the RNA representative good stability of this molecule. In silico scanning was done to predict the 3D structure of the protein, and modeling was validated using the Ramachandran plot analysis. The chimeric protein (rCCL) was expressed in a prokaryotic expression system (E. coli), purified, and analyzed for their immunogenic properties. It was revealed that the production of a high titer of antibody produced in immunized mice could neutralize the ETEC using the rabbit ileal loop tests. The results indicated that the protein inferred from the recombinant protein (rCCL) construct could act as a proper vaccine candidate against three critical causative agents of diarrheal bacteria at the same time.

Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution

Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date MHC I binding prediction tools developed over the last decade, thoroughly evaluating feature representation methods, prediction algorithms and model training strategies on a benchmark dataset from Immune Epitope Database. A common limitation was identified during the review that all existing tools can only handle a fixed peptide sequence length. To overcome this limitation, we developed a bilateral and variable long short-term memory (BVLSTM)-based approach, named BVLSTM-MHC. It is the first variable-length MHC class I binding predictor. In comparison to the 10 mainstream prediction tools on an independent validation dataset, BVLSTM-MHC achieved the best performance in six out of eight evaluated metrics. A web server based on the BVLSTM-MHC model was developed to enable accurate and efficient MHC class I binder prediction in human, mouse, macaque and chimpanzee.

Discordance Between the Predicted Versus the Actually Recognized CD8+ T Cell Epitopes of HCMV pp65 Antigen and Aleatory Epitope Dominance

CD8+ T cell immune monitoring aims at measuring the size and functions of antigen-specific CD8+ T cell populations, thereby providing insights into cell-mediated immunity operational in a test subject. The selection of peptides for ex vivo CD8+ T cell detection is critical because within a complex antigen exists a multitude of potential epitopes that can be presented by HLA class I molecules. Further complicating this task, there is HLA class I polygenism and polymorphism which predisposes CD8+ T cell responses towards individualized epitope recognition profiles. In this study, we compare the actual CD8+ T cell recognition of a well-characterized model antigen, human cytomegalovirus (HCMV) pp65 protein, with its anticipated epitope coverage. Due to the abundance of experimentally defined HLA-A*02:01-restricted pp65 epitopes, and because in silico epitope predictions are most advanced for HLA-A*02:01, we elected to focus on subjects expressing this allele. In each test subject, every possible CD8+ T cell epitope was systematically covered testing 553 individual peptides that walk the sequence of pp65 in steps of single amino acids. Highly individualized CD8+ T cell response profiles with aleatory epitope recognition patterns were observed. No correlation was found between epitopes' ranking on the prediction scale and their actual immune dominance. Collectively, these data suggest that accurate CD8+ T cell immune monitoring may necessitate reliance on agnostic mega peptide pools, or brute force mapping, rather than electing individual peptides as representative epitopes for tetramer and other multimer labeling of surface antigen receptors.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Techniques, perspectives, and challenges of bioactive peptide generation: A comprehensive systematic review

Due to the digestible refractory and absorbable structures of bioactive peptides (BPs), they could induce notable biological impacts on the living organism. In this regard, the current study was devoted to providing an overview regarding the available methods for BPs generation by the aid of a systematic review conducted on the published articles up to April 2019. In this context, the PubMed and Scopus databases were screened to retrieve the related publications. According to the results, although the characterization of BPs mainly has been performed using enzymatic and microbial in-vitro methods, they cannot be considered as suitable techniques for further stimulation of digestion in the gastrointestinal tract. Therefore, new approaches for both in-vivo and in-silico methods for BPs identification should be developed to overcome the obstacles that belonged to the current methods. The purpose of this review was to compile the recent analytical methods applied for studying various aspects of food-derived biopeptides, and emphasizing generation at in vitro, in vivo, and in silico.

A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

Human leukocyte antigen class I (HLA-I) molecules are encoded by major histocompatibility complex (MHC) class I loci in humans. The binding and interaction between HLA-I molecules and intracellular peptides derived from a variety of proteolytic mechanisms play a crucial role in subsequent T-cell recognition of target cells and the specificity of the immune response. In this context, tools that predict the likelihood for a peptide to bind to specific HLA class I allotypes are important for selecting the most promising antigenic targets for immunotherapy. In this article, we comprehensively review a variety of currently available tools for predicting the binding of peptides to a selection of HLA-I allomorphs. Specifically, we compare their calculation methods for the prediction score, employed algorithms, evaluation strategies and software functionalities. In addition, we have evaluated the prediction performance of the reviewed tools based on an independent validation data set, containing 21 101 experimentally verified ligands across 19 HLA-I allotypes. The benchmarking results show that MixMHCpred 2.0.1 achieves the best performance for predicting peptides binding to most of the HLA-I allomorphs studied, while NetMHCpan 4.0 and NetMHCcons 1.1 outperform the other machine learning-based and consensus-based tools, respectively. Importantly, it should be noted that a peptide predicted with a higher binding score for a specific HLA allotype does not necessarily imply it will be immunogenic. That said, peptide-binding predictors are still very useful in that they can help to significantly reduce the large number of epitope candidates that need to be experimentally verified. Several other factors, including susceptibility to proteasome cleavage, peptide transport into the endoplasmic reticulum and T-cell receptor repertoire, also contribute to the immunogenicity of peptide antigens, and some of them can be considered by some predictors. Therefore, integrating features derived from these additional factors together with HLA-binding properties by using machine-learning algorithms may increase the prediction accuracy of immunogenic peptides. As such, we anticipate that this review and benchmarking survey will assist researchers in selecting appropriate prediction tools that best suit their purposes and provide useful guidelines for the development of improved antigen predictors in the future.

Immunoinformatics and Epitope Prediction

With advancements in sequencing technologies, vast amount of experimental data has accumulated. Due to rapid progress in the development of bioinformatics tools and the accumulation of data, immunoinformatics or computational immunology emerged as a special branch of bioinformatics which utilizes bioinformatics approaches for understanding and interpreting immunological data. One extensively studied aspect of applied immunology involves using available databases and tools for prediction of B- and T-cell epitopes. B and T cells comprise two arms of adaptive immunity.This chapter first reviews the methodology we used for computational identification of B- and T-cell epitopes against enterotoxigenic Escherichia coli (ETEC). Then we discuss other databases of epitopes and analysis tools for T-cell and B-cell epitope prediction and vaccine design. The predicted peptides were analyzed for conservation and population coverage. HLA distribution analysis for predicted epitopes identified efficient MHC binders. Epitopes were further tested using computational docking studies to bind in MHC-I molecule cleft. The predicted epitopes were conserved and covered more than 80% of the world population.

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this “convergence” of adaptive immunity among different individuals remain poorly understood. To quantitatively describe epitope immunogenicity, here we propose a supervised machine learning framework generating probabilistic estimates of immunogenicity, termed “immunogenicity scores,” based on the numerical features computed through sequence-based simulation approximating the molecular scanning process of peptides presented onto major histocompatibility complex (MHC) by the human T cell receptor (TCR) repertoire. Notably, overlapping sets of intermolecular interaction parameters were commonly utilized in MHC-I and MHC-II prediction. Moreover, a similar simulation of individual TCR-peptide interaction using the same set of interaction parameters yielded correlates of TCR affinity. Pathogen-derived epitopes and tumor-associated epitopes with positive T cell reactivity generally had higher immunogenicity scores than non-immunogenic counterparts, whereas thymically expressed self-epitopes were assigned relatively low scores regardless of their immunogenicity annotation. Immunogenicity score dynamics among single amino acid mutants delineated the landscape of position- and residue-specific mutational impacts. Simulation of position-specific immunogenicity score dynamics detected residues with high escape potential in multiple epitopes, consistent with known escape mutations in the literature. This study indicates that targeting of epitopes by human adaptive immunity is to some extent directed by defined thermodynamic principles. The proposed framework also has a practical implication in that it may enable to more efficiently prioritize epitope candidates highly prone to T cell recognition in multiple individuals, warranting prospective validation across different cohorts.

Bioinformatics Applications in Advancing Animal Virus Research

Viruses serve as infectious agents for all living entities. There have been various research groups that focus on understanding the viruses in terms of their host-viral relationships, pathogenesis and immune evasion. However, with the current advances in the field of science, now the research field has widened up at the ‘omics’ level. Apparently, generation of viral sequence data has been increasing. There are numerous bioinformatics tools available that not only aid in analysing such sequence data but also aid in deducing useful information that can be exploited in developing preventive and therapeutic measures. This chapter elaborates on bioinformatics tools that are specifically designed for animal viruses as well as other generic tools that can be exploited to study animal viruses. The chapter further provides information on the tools that can be used to study viral epidemiology, phylogenetic analysis, structural modelling of proteins, epitope recognition and open reading frame (ORF) recognition and tools that enable to analyse host-viral interactions, gene prediction in the viral genome, etc. Various databases that organize information on animal and human viruses have also been described. The chapter will converse on overview of the current advances, online and downloadable tools and databases in the field of bioinformatics that will enable the researchers to study animal viruses at gene level.

In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs

The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.

Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics

The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.

Computer-Aided Design of an Epitope-Based Vaccine against Epstein-Barr Virus

Epstein-Barr virus is a very common human virus that infects 90% of human adults. EBV replicates in epithelial and B cells and causes infectious mononucleosis. EBV infection is also linked to various cancers, including Burkitt's lymphoma and nasopharyngeal carcinomas, and autoimmune diseases such as multiple sclerosis. Currently, there are no effective drugs or vaccines to treat or prevent EBV infection. Herein, we applied a computer-aided strategy to design a prophylactic epitope vaccine ensemble from experimentally defined T and B cell epitopes. Such strategy relies on identifying conserved epitopes in conjunction with predictions of HLA presentation for T cell epitope selection and calculations of accessibility and flexibility for B cell epitope selection. The T cell component includes 14 CD8 T cell epitopes from early antigens and 4 CD4 T cell epitopes, targeted during the course of a natural infection and providing a population protection coverage of over 95% and 81.8%, respectively. The B cell component consists of 3 experimentally defined B cell epitopes from gp350 plus 4 predicted B cell epitopes from other EBV envelope glycoproteins, all mapping in flexible and solvent accessible regions. We discuss the rationale for the formulation and possible deployment of this epitope vaccine ensemble.

A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.

EPIPOX: Immunoinformatic Characterization of the Shared T-Cell Epitome between Variola Virus and Related Pathogenic Orthopoxviruses

Concerns that variola viruses might be used as bioweapons have renewed the interest in developing new and safer smallpox vaccines. Variola virus genomes are now widely available, allowing computational characterization of the entire T-cell epitome and the use of such information to develop safe and yet effective vaccines. To this end, we identified 124 proteins shared between various species of pathogenic orthopoxviruses including variola minor and major, monkeypox, cowpox, and vaccinia viruses, and we targeted them for T-cell epitope prediction. We recognized 8,106, and 8,483 unique class I and class II MHC-restricted T-cell epitopes that are shared by all mentioned orthopoxviruses. Subsequently, we developed an immunological resource, EPIPOX, upon the predicted T-cell epitome. EPIPOX is freely available online and it has been designed to facilitate reverse vaccinology. Thus, EPIPOX includes key epitope-focused protein annotations: time point expression, presence of leader and transmembrane signals, and known location on outer membrane structures of the infective viruses. These features can be used to select specific T-cell epitopes suitable for experimental validation restricted by single MHC alleles, as combinations thereof, or by MHC supertypes.

Databases for T-cell epitopes

Modem immunology and vaccinology incorporate immunoinformatics techniques to give insights into immune systems and accelerate vaccine design. Databases managing epitope data in a structured form with immune-related annotations including sequences, alleles, source organisms, structures, and diseases could be the most crucial part of immunoinformatics offering data sources for the analysis of immune systems and development of prediction methods. This chapter provides an overview of publicly available databases of T-cell epitopes including general databases, pathogen- and tumor-specific databases, and 3D structure databases.

T-cell epitope prediction methods: an overview

The scientific community is overwhelmed by the voluminous increase in the quantum of data on biological systems, including but not limited to the immune system. Consequently, immunoinformatics databases are continually being developed to accommodate this ever increasing data and analytical tools are continually being developed to analyze the same. Therefore, researchers are now equipped with numerous databases, analytical and prediction tools, in anticipation of better means of prevention of and therapeutic intervention in diseases of humans and other animals. Epitope is a part of an antigen, recognized either by B- or T-cells and/or molecules of the host immune system. Since only a few amino acid residues that comprise an epitope (instead of the whole protein) are sufficient to elicit an immune response, attempts are being made to identify or predict this critical stretch or patch of amino acid residues, i.e., T-cell epitopes and B-cell epitopes to be included in multiple-subunit vaccines. T-cell epitope prediction is a challenge owing to the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. Many algorithms/methods developed to predict T-cell epitopes and Web servers incorporating the same are available. These are based on approaches like considering amphipathicity profiles of proteins, sequence motifs, quantitative matrices (QM), artificial neural networks (ANN), support vector machines (SVM), quantitative structure activity relationship (QSAR) and molecular docking simulations, etc. This chapter aims to introduce the reader to the principle(s) underlying some of these methods/algorithms as well as procedural and practical aspects of using the same.

Selection of conserved epitopes from hepatitis C virus for pan-populational stimulation of T-cell responses

The hepatitis C virus (HCV) is able to persist as a chronic infection, which can lead to cirrhosis and liver cancer. There is evidence that clearance of HCV is linked to strong responses by CD8 cytotoxic T lymphocytes (CTLs), suggesting that eliciting CTL responses against HCV through an epitope-based vaccine could prove an effective means of immunization. However, HCV genomic plasticity as well as the polymorphisms of HLA I molecules restricting CD8 T-cell responses challenges the selection of epitopes for a widely protective vaccine. Here, we devised an approach to overcome these limitations. From available databases, we first collected a set of 245 HCV-specific CD8 T-cell epitopes, all known to be targeted in the course of a natural infection in humans. After a sequence variability analysis, we next identified 17 highly invariant epitopes. Subsequently, we predicted the epitope HLA I binding profiles that determine their potential presentation and recognition. Finally, using the relevant HLA I-genetic frequencies, we identified various epitope subsets encompassing 6 conserved HCV-specific CTL epitopes each predicted to elicit an effective T-cell response in any individual regardless of their HLA I background. We implemented this epitope selection approach for free public use at the EPISOPT web server.

An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles

Background

The immune system must detect a wide variety of microbial pathogens, such as viruses, bacteria, fungi and parasitic worms, to protect the host against disease. Antigenic peptides displayed by MHC II (class II Major Histocompatibility Complex) molecules is a pivotal process to activate CD4+ T_H cells (Helper T cells). The activated T_H cells can differentiate into effector cells which assist various cells in activating against pathogen invasion. Each MHC locus encodes a great number of allele variants. Yet this limited number of MHC molecules are required to display enormous number of antigenic peptides. Since the peptide binding measurements of MHC molecules by biochemical experiments are expensive, only a few of the MHC molecules have suffecient measured peptides. To perform accurate binding prediction for those MHC alleles without suffecient measured peptides, a number of computational algorithms were proposed in the last decades.

Results

Here, we propose a new MHC II binding prediction approach, OWA-PSSM, which is a significantly extended version of a well known method called TEPITOPE. The TEPITOPE method is able to perform prediction for only 50 MHC alleles, while OWA-PSSM is able to perform prediction for much more, up to 879 HLA-DR molecules. We evaluate the method on five benchmark datasets. The method is demonstrated to be the best one in identifying binding cores compared with several other popular state-of-the-art approaches. Meanwhile, the method performs comparably to the TEPITOPE and NetMHCIIpan2.0 approaches in identifying HLA-DR epitopes and ligands, and it performs significantly better than TEPITOPEpan in the identification of HLA-DR ligands and MultiRTA in identifying HLA-DR T cell epitopes.

Conclusions

The proposed approach OWA-PSSM is fast and robust in identifying ligands, epitopes and binding cores for up to 879 MHC II molecules.

Using databases and data mining in vaccinology

Throughout time functional immunology has accumulated vast amounts of quantitative and qualitative data relevant to the design and discovery of vaccines. Such data includes, but is not limited to, components of the host and pathogen genome (including antigens and virulence factors), T- and B-cell epitopes and other components of the antigen presentation pathway and allergens. In this review the authors discuss a range of databases that archive such data. Built on such information, increasingly sophisticated data mining techniques have developed that create predictive models of utilitarian value. With special reference to epitope data, the authors discuss the strengths and weaknesses of the available techniques and how they can aid computer-aided vaccine design deliver added value for vaccinology.

PREDIVAC: CD4+ T-cell epitope prediction for vaccine design that covers 95% of HLA class II DR protein diversity

Background

CD4+ T-cell epitopes play a crucial role in eliciting vigorous protective immune responses during peptide (epitope)-based vaccination. The prediction of these epitopes focuses on the peptide binding process by MHC class II proteins. The ability to account for MHC class II polymorphism is critical for epitope-based vaccine design tools, as different allelic variants can have different peptide repertoires. In addition, the specificity of CD4+ T-cells is often directed to a very limited set of immunodominant peptides in pathogen proteins. The ability to predict what epitopes are most likely to dominate an immune response remains a challenge.

Results

We developed the computational tool Predivac to predict CD4+ T-cell epitopes. Predivac can make predictions for 95% of all MHC class II protein variants (allotypes), a substantial advance over other available methods. Predivac bases its prediction on the concept of specificity-determining residues. The performance of the method was assessed both for high-affinity HLA class II peptide binding and CD4+ T-cell epitope prediction. In terms of epitope prediction, Predivac outperformed three available pan-specific approaches (delivering the highest specificity). A central finding was the high accuracy delivered by the method in the identification of immunodominant and promiscuous CD4+ T-cell epitopes, which play an essential role in epitope-based vaccine design.

Conclusions

The comprehensive HLA class II allele coverage along with the high specificity in identifying immunodominant CD4+ T-cell epitopes makes Predivac a valuable tool to aid epitope-based vaccine design in the context of a genetically heterogeneous human population.The tool is available at: http://predivac.biosci.uq.edu.au/.

Identification of Candidate Vaccine Antigens In Silico

The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.

Bioinformatics for cancer immunology and immunotherapy

Recent mechanistic insights obtained from preclinical studies and the approval of the first immunotherapies has motivated increasing number of academic investigators and pharmaceutical/biotech companies to further elucidate the role of immunity in tumor pathogenesis and to reconsider the role of immunotherapy. Additionally, technological advances (e.g., next-generation sequencing) are providing unprecedented opportunities to draw a comprehensive picture of the tumor genomics landscape and ultimately enable individualized treatment. However, the increasing complexity of the generated data and the plethora of bioinformatics methods and tools pose considerable challenges to both tumor immunologists and clinical oncologists. In this review, we describe current concepts and future challenges for the management and analysis of data for cancer immunology and immunotherapy. We first highlight publicly available databases with specific focus on cancer immunology including databases for somatic mutations and epitope databases. We then give an overview of the bioinformatics methods for the analysis of next-generation sequencing data (whole-genome and exome sequencing), epitope prediction tools as well as methods for integrative data analysis and network modeling. Mathematical models are powerful tools that can predict and explain important patterns in the genetic and clinical progression of cancer. Therefore, a survey of mathematical models for tumor evolution and tumor-immune cell interaction is included. Finally, we discuss future challenges for individualized immunotherapy and suggest how a combined computational/experimental approaches can lead to new insights into the molecular mechanisms of cancer, improved diagnosis, and prognosis of the disease and pinpoint novel therapeutic targets.

CD8 T cell epitope distribution in viruses reveals patterns of protein biosynthesis

Distinguishing T cell epitope distribution patterns is relevant for epitope-vaccine design. To that end, we invest0069gated the distribution of known CD8 T cell epitopes from Hepatitis C Virus, Human Immunodeficiency Virus-1 and Influenza A Virus using χ² statistics. We found that epitopes are not distributed in the viral proteomes proportionally to the size of the source proteins. Specifically, capsid and matrix proteins pack significantly more epitopes than those expected by their size. Such non-homogeneous distribution cannot be accounted by underlying MHC I-peptide binding preferences nor it is related to sequence variability. Instead, we propose that it might be related to preferential protein translation/biosynthesis. Overall, these results support the prioritization of structural antigens for epitope identification and vaccine design.

Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools

Binding of short antigenic peptides to major histocompatibility complex (MHC) molecules is a core step in adaptive immune response. Precise identification of MHC-restricted peptides is of great significance for understanding the mechanism of immune response and promoting the discovery of immunogenic epitopes. However, due to the extremely high MHC polymorphism and huge cost of biochemical experiments, there is no experimentally measured binding data for most MHC molecules. To address the problem of predicting peptides binding to these MHC molecules, recently computational approaches, called pan-specific methods, have received keen interest. Pan-specific methods make use of experimentally obtained binding data of multiple alleles, by which binding peptides (binders) of not only these alleles but also those alleles with no known binders can be predicted. To investigate the possibility of further improvement in performance and usability of pan-specific methods, this article extensively reviews existing pan-specific methods and their web servers. We first present a general framework of pan-specific methods. Then, the strategies and performance as well as utilities of web servers are compared. Finally, we discuss the future direction to improve pan-specific methods for MHC-peptide binding prediction.

Emerging vaccine informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

HLA class I-associated diseases with a suspected autoimmune etiology: HLA-B27 subtypes as a model system

Although most autoimmune diseases are connected to major histocompatibility complex (MHC) class II alleles, a small number of these disorders exhibit a variable degree of association with selected MHC class I genes, like certain human HLA-A and HLA-B alleles. The basis for these associations, however, has so far remained elusive. An understanding might be obtained by comparing functional, biochemical, and biophysical properties of alleles that are minimally distinct from each other, but are nevertheless differentially associated to a given disease, like the HLA-B*27:05 and HLA-B*27:09 antigens, which differ only by a single amino acid residue (Asp116His) that is deeply buried within the binding groove. We have employed a number of approaches, including X-ray crystallography and isotope-edited infrared spectroscopy, to investigate biophysical characteristics of the two HLA-B27 subtypes complexed with up to ten different peptides. Our findings demonstrate that the binding of these peptides as well as the conformational flexibility of the subtypes is greatly influenced by interactions of the C-terminal peptide residue. In particular, a basic C-terminal peptide residue is favoured by the disease-associated subtype HLA-B*27:05, but not by HLA-B*27:09. This property appears also as the only common denominator of distinct HLA class I alleles, among them HLA-B*27:05, HLA-A*03:01 or HLA-A*11:01, that are associated with diseases suspected to have an autoimmune etiology. We postulate here that the products of these alleles, due to their unusual ability to bind with high affinity to a particular peptide set during positive T cell selection in the thymus, are involved in shaping an abnormal T cell repertoire which predisposes to the acquisition of autoimmune diseases.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

Computational analysis and modeling of cleavage by the immunoproteasome and the constitutive proteasome

Background

Proteasomes play a central role in the major histocompatibility class I (MHCI) antigen processing pathway. They conduct the proteolytic degradation of proteins in the cytosol, generating the C-terminus of CD8 T cell epitopes and MHCI-peptide ligands (P1 residue of cleavage site). There are two types of proteasomes, the constitutive form, expressed in most cell types, and the immunoproteasome, which is constitutively expressed in mature dendritic cells. Protective CD8 T cell epitopes are likely generated by the immunoproteasome and the constitutive proteasome, and here we have modeled and analyzed the cleavage by these two proteases.

Results

We have modeled the immunoproteasome and proteasome cleavage sites upon two non-overlapping sets of peptides consisting of 553 CD8 T cell epitopes, naturally processed and restricted by human MHCI molecules, and 382 peptides eluted from human MHCI molecules, respectively, using N-grams. Cleavage models were generated considering different epitope and MHCI-eluted fragment lengths and the same number of C-terminal flanking residues. Models were evaluated in 5-fold cross-validation. Judging by the Mathew's Correlation Coefficient (MCC), optimal cleavage models for the proteasome (MCC = 0.43 ± 0.07) and the immunoproteasome (MCC = 0.36 ± 0.06) were obtained from 12-residue peptide fragments. Using an independent dataset consisting of 137 HIV1-specific CD8 T cell epitopes, the immunoproteasome and proteasome cleavage models achieved MCC values of 0.30 and 0.18, respectively, comparatively better than those achieved by related methods. Using ROC analyses, we have also shown that, combined with MHCI-peptide binding predictions, cleavage predictions by the immunoproteasome and proteasome models significantly increase the discovery rate of CD8 T cell epitopes restricted by different MHCI molecules, including A*0201, A*0301, A*2402, B*0702, B*2705.

Conclusions

We have developed models that are specific to predict cleavage by the proteasome and the immunoproteasome. These models ought to be instrumental to identify protective CD8 T cell epitopes and are readily available for free public use at http://imed.med.ucm.es/Tools/PCPS/.

MHC I stabilizing potential of computer-designed octapeptides

Experimental results are presented for 180 in silico designed octapeptide sequences and their stabilizing effects on the major histocompatibility class I molecule H-2K^b. Peptide sequence design was accomplished by a combination of an ant colony optimization algorithm with artificial neural network classifiers. Experimental tests yielded nine H-2K^b stabilizing and 171 nonstabilizing peptides. 28 among the nonstabilizing octapeptides contain canonical motif residues known to be favorable for MHC I stabilization. For characterization of the area covered by stabilizing and non-stabilizing octapeptides in sequence space, we visualized the distribution of 100,603 octapeptides using a self-organizing map. The experimental results present evidence that the canonical sequence motives of the SYFPEITHI database on their own are insufficient for predicting MHC I protein stabilization.

Computational immunology meets bioinformatics: the use of prediction tools for molecular binding in the simulation of the immune system

We present a new approach to the study of the immune system that combines techniques of systems biology with information provided by data-driven prediction methods. To this end, we have extended an agent-based simulator of the immune response, C-ImmSim, such that it represents pathogens, as well as lymphocytes receptors, by means of their amino acid sequences and makes use of bioinformatics methods for T and B cell epitope prediction. This is a key step for the simulation of the immune response, because it determines immunogenicity. The binding of the epitope, which is the immunogenic part of an invading pathogen, together with activation and cooperation from T helper cells, is required to trigger an immune response in the affected host. To determine a pathogen's epitopes, we use existing prediction methods. In addition, we propose a novel method, which uses Miyazawa and Jernigan protein-protein potential measurements, for assessing molecular binding in the context of immune complexes. We benchmark the resulting model by simulating a classical immunization experiment that reproduces the development of immune memory. We also investigate the role of major histocompatibility complex (MHC) haplotype heterozygosity and homozygosity with respect to the influenza virus and show that there is an advantage to heterozygosity. Finally, we investigate the emergence of one or more dominating clones of lymphocytes in the situation of chronic exposure to the same immunogenic molecule and show that high affinity clones proliferate more than any other. These results show that the simulator produces dynamics that are stable and consistent with basic immunological knowledge. We believe that the combination of genomic information and simulation of the dynamics of the immune system, in one single tool, can offer new perspectives for a better understanding of the immune system.

Quantitative modeling of peptide binding to TAP using support vector machine

The transport of peptides to the endoplasmic reticulum by the transporter associated with antigen processing (TAP) is a necessary step towards determining CD8 T cell epitopes. In this work, we have studied the predictive performance of support vector machine models trained on single residue positions and residue combinations drawn from a large dataset consisting of 613 nonamer peptides of known affinity to TAP. Predictive performance of these TAP affinity models was evaluated under 10-fold cross-validation experiments and measured using Pearson's correlation coefficients (R(p)). Our results show that every peptide position (P1-P9) contributes to TAP binding (minimum R(p) of 0.26 +/- 0.11 was achieved by a model trained on the P6 residue), although the largest contributions to binding correspond to the C-terminal end (R(p) = 0.68 +/- 0.06) and the P1 (R(p) = 0.51 +/- 0.09) and P2 (0.57 +/- 0.08) residues of the peptide. Training the models on additional peptide residues generally improved their predictive performance and a maximum correlation (R(p) = 0.89 +/- 0.03) was achieved by a model trained on the full-length sequences or a residue selection consisting of the first 5 N- and last 3 C-terminal residues of the peptides included in the training set. A system for predicting the binding affinity of peptides to TAP using the methods described here is readily available for free public use at http://imed.med.ucm.es/Tools/tapreg/.

Design and utilization of epitope-based databases and predictive tools

In the last decade, significant progress has been made in expanding the scope and depth of publicly available immunological databases and online analysis resources, which have become an integral part of the repertoire of tools available to the scientific community for basic and applied research. Herein, we present a general overview of different resources and databases currently available. Because of our association with the Immune Epitope Database and Analysis Resource, this resource is reviewed in more detail. Our review includes aspects such as the development of formal ontologies and the type and breadth of analytical tools available to predict epitopes and analyze immune epitope data. A common feature of immunological databases is the requirement to host large amounts of data extracted from disparate sources. Accordingly, we discuss and review processes to curate the immunological literature, as well as examples of how the curated data can be used to generate a meta-analysis of the epitope knowledge currently available for diseases of worldwide concern, such as influenza and malaria. Finally, we review the impact of immunological databases, by analyzing their usage and citations, and by categorizing the type of citations. Taken together, the results highlight the growing impact and utility of immunological databases for the scientific community.

Immunoinformatics and modeling perspective of T cell epitope-based cancer immunotherapy: a holistic picture

Cancer immunotherapy is fast gaining global attention with its unique position as a potential therapy showing promise in cancer prevention and cure. It utilizes the natural system of immunity as opposed to chemotherapy and radiotherapy that utilize chemical drugs and radiation, respectively. Cancer immunotherapy essentially involves treatment and/or prevention with vaccines in the form of peptide vaccines (T and B cell epitopes), DNA vaccines and vaccination using whole tumor cells, dendritic cells, viral vectors, antibodies and adoptive transfer of T cells to harness the body's own immune system towards the targeting of cancer cells for destruction. Given the time, cost and labor involved in the vaccine discovery and development, researchers have evinced interest in the novel field of immunoinformatics to cut down the escalation of these critical resources. Immunoinformatics is a relatively new buzzword in the scientific circuit that is showing its potential and delivering on its promise in expediting the development of effective cancer immunotherapeutic agents. This review attempts to present a holistic picture of our race against cancer and time using the science and technology of immunoinformatics and molecular modeling in T cell epitope-based cancer immunotherapy. It also attempts to showcase some problem areas as well as novel ones waiting to be explored where development of novel immunoinformatics tools and simulations in the context of cancer immunotherapy would be highly welcome.

An introduction to epitope prediction methods and software

In this paper, current prediction methods and algorithms for both T- and B cell epitopes are reviewed, and a comprehensive summary of epitope prediction software and databases currently available online is also provided. This review can offer researchers in this field a sense of direction and insights for future work. However, our main purpose is to introduce clinical and basic biomedical researchers who are not familiar with these biological analysis tools and databases to these online resources and to provide guidance on how to use them effectively.

Low-affinity peptides and T-cell selection

The dual requirement for T cells to recognize a particular major histocompatibility complex (MHC) antigen presenting a foreign peptide and to lack strong reactivity with a complex of the same molecule when bound to a self-peptide, is attained by thymic positive and negative selection processes, the molecular details of which are currently only partially understood. However, the discovery of the thymoproteasome and our improved understanding of the dynamics of peptide presentation permit us to suggest that the biophysical properties of the MHC:peptide class I complexes engaged in positive T-cell selection will be distinct from those involved in negative selection, hence imposing differential barriers for T cells.

PepBank--a database of peptides based on sequence text mining and public peptide data sources

Background

Peptides are important molecules with diverse biological functions and biomedical uses. To date, there does not exist a single, searchable archive for peptide sequences or associated biological data. Rather, peptide sequences still have to be mined from abstracts and full-length articles, and/or obtained from the fragmented public sources.

Description

We have constructed a new database (PepBank), which at the time of writing contains a total of 19,792 individual peptide entries. The database has a web-based user interface with a simple, Google-like search function, advanced text search, and BLAST and Smith-Waterman search capabilities. The major source of peptide sequence data comes from text mining of MEDLINE abstracts. Another component of the database is the peptide sequence data from public sources (ASPD and UniProt). An additional, smaller part of the database is manually curated from sets of full text articles and text mining results. We show the utility of the database in different examples of affinity ligand discovery.

Conclusion

We have created and maintain a database of peptide sequences. The database has biological and medical applications, for example, to predict the binding partners of biologically interesting peptides, to develop peptide based therapeutic or diagnostic agents, or to predict molecular targets or binding specificities of peptides resulting from phage display selection. The database is freely available on , and the text mining source code (Peptide::Pubmed) is freely available above as well as on CPAN ().